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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
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NASA NTRS2000

Summary Report of Mission Acceleration Measurements for STS-95

John H. Glenn's historic return to space was a primary focus of the STS-95 mission. The Hubble Space Telescope (HST) Orbital Systems Test (HOST). an STS-95 payload, was an in-flight demonstration of HST components to be installed during the next HST servicing mission. One of the components under evaluation was the cryocooler for the Near Infrared Camera and Multi-Object Spectrometer (NICMOS). Based on concerns about vibrations from the operation of the NICMOS cryocooler affecting the overall HST line-of-sight requirements, the Space Acceleration Measurement System for Free-Flyers (SAMS-FF) was employed to measure the vibratory environment of the STS-95 mission, including any effects introduced by the NICMOS cryocooler. The STS-95 mission represents the first STS mission supported by SAMS-FF. Utilizing a Control and Data Acquisition Unit (CDU) and two triaxial sensor heads (TSH) mounted on the HOST support structure in Discovery's cargo bay, the SAMS-FF and the HOST project were able to make vibratory measurements both on-board the vibration-isolated NICMOS cryocooler and off-board the cryocooler mounting plate. By comparing the SAMS-FF measured vibrations on-board and off-board the NICMOS cryocooler, HST engineers could assess the cryocooler g-jitter effects on the HST line-of-sight requirements. The acceleration records from both SAMS-FF accelerometers were analyzed and significant features of the microgravity environment are detailed in this report.

McPherson, Kevin↗
NASA NTRS2011

The James Webb Space Telescope

The James Webb Space Telescope is the scientific successor to the Hubble and Spitzer Space Telescopes, and is currently the largest scientific project under construction in the United States. It will be a large (6.6m) cold (50K) telescope launched into orbit around the second Earth-Sun Lagrange point. It is a partnership of NASA with the European and Canadian Space Agencies. Science with the James Webb Space Telescope falls into four themes. The End of the Dark Ages: First Light and Reionization theme seeks to identify the first luminous sources to form and to determine the ionization history of the universe. The Assembly of Galaxies theme seeks to determine how galaxies and the dark matter, gas, stars, metals, morphological structures, and black holes within them evolved from the epoch of reionization to the present. The Birth of Stars and Protoplanetary Systems theme seeks to unravel the birth and early evolution of stars, from infall onto dust-enshrouded protostars, to the genesis of planetary systems. The Planetary Systems and the Origins of Life theme seeks to determine the physical and chemical properties of planetary systems around nearby stars and of our own, and investigate the potential for life in those systems. Webb will have four instruments: The Near-Infrared Camera, the Near-Infrared multi-object Spectrograph, and the Tunable Filter Imager will cover the wavelength range 0.6 to 5 microns, while the Mid-Infrared Instrument will do both imaging and spectroscopy from 5 to 28.5 microns. I will conclude the talk with a description of recent technical progress in the construction of the observatory.

Gardner, Jonathan P.↗
NASA NTRS2011

Status Update on the James Webb Space Telescope Project

The James Webb Space Telescope (JWST) is a large (6.6 m), cold (<50 K), infrared (IR)-optimized space observatory that will be launched in approx.2018. The observatory will have four instruments covering 0.6 to 28 micron, including a multi-object spectrograph, two integral fie ld units, and grisms optimized for exoplanets. I will review JWST's k ey science themes, as well as exciting new ideas from the recent JWST Frontiers Workshop. I will summarize the technical progress and miss ion status. Recent highlights: All mirrors have been fabricated, polished, and gold-coated; the mirror is expected to be diffraction-limite d down to a wavelength of 2 micron. The MIRI instrument just complete d its cryogenic testing. STScI has released exposure time calculators and sensitivity charts to enable scientists to start thinking about how to use JWST for their science.

Rigby, Jane R.↗
NASA NTRS2011

Overview of the James Webb Space Telescope Observatory

The James Webb Space Telescope (JWST) is a large aperture, space telescope designed to provide imaging and spectroscopy from 1.0 micron to 28 microns. JWST will be launched to an orbit at L2 aboard an Ariane 5 launcher in 2013. The Goddard Space Flight Center (GSFC) is the lead center for the JWST program and manages the project for NASA. The prime contractor for JWST is Northrop Grumman Aerospace Systems (NGST). JWST is an international partnership with the European Space Agency (ESA), and the Canadian Space Agency (CSA). ESA will contribute the Ariane 5 launch, and a multi-object infrared spectrograph. CSA will contribute the Fine Guidance Sensor (FGS), which includes the Tunable Filter Imager (TFI). The European consortium, in collaboration with the Jet Propulsion Laboratory (JPL), builds the mid-infrared imager (MIRI). In this paper we present an overview of the JWST science program, and discuss recent progress in the development of the observatory.

Clampin, Mark↗
NASA NTRS2011

The James Webb Space Telescope: Mission Overview and Status

The James Webb Space Telescope (JWST) is the Infrared successor to the Hubble Space Telescope. It is a cryogenic infrared space observatory with a 25 sq m aperture (6 m class) telescope yielding diffraction limited angular resolution at a wave1ength of 2 micron. The science instrument payload includes three passively cooled near-infrared instruments providing broad- and narrow-band imagery, coronagraphy, as well as multi-object and integral-field spectroscopy over the 0.6 <lambda < 5.0 micron spectrum. An actively cooled mid-infrared instrument provides broad-band imagery, coronagraphy, and integral-field spectroscopy over the 5.0 < lambda < 29 micron spectrum. The JWST is being developed by NASA, in partnership with the European and Canadian Space Agencies, as a general user facility with science observations to be proposed by the international astronomical community in a manner similar to the Hubble Space Telescope. Technology development and mission design are complete, and construction is underway in all areas of the program.

Greenhouse, Matthew A.↗
NASA NTRS2011

Fabrication and Calibration of FORTIS

The Johns Hopkins University sounding rocket group is entering the final fabrication phase of the Far-ultraviolet Off Rowland-circle Telescope for Imaging and Spectroscopy (FORTIS); a sounding rocket borne multi-object spectro-telescope designed to provide spectral coverage of 43 separate targets in the 900 - 1800 Angstrom bandpass over a 30' x 30' field-of-view. Using "on-the-fly" target acquisition and spectral multiplexing enabled by a GSFC microshutter array, FORTIS will be capable of observing the brightest regions in the far-UV of nearby low redshift (z approximately 0.002 - 0.02) star forming galaxies to search for Lyman alpha escape, and to measure the local gas-to-dust ratio. A large area (approximately 45 mm x 170 mm) microchannel plate detector built by Sensor Sciences provides an imaging channel for targeting flanked by two redundant spectral outrigger channels. The grating is ruled directly onto the secondary mirror to increase efficiency. In this paper, we discuss the recent progress made in the development and fabrication of FORTIS, as well as the results of early calibration and characterization of our hardware, including mirror/grating measurements, detector performance, and early operational tests of the micro shutter arrays.

Fleming, Brian T.↗
NASA NTRS2011

Mechanical Design of NESSI: New Mexico Tech Extrasolar Spectroscopic Survey Instrument

NESSI: the New Mexico Tech Extrasolar Spectroscopic Survey Instrument is a ground-based multi-object spectrograph that operates in the near-infrared. It will be installed on one of the Nasmyth ports of the Magdalena Ridge Observatory (MRO) 2.4-meter Telescope sited in the Magdalena Mountains, about 48 km west of Socorro-NM. NESSI operates stationary to the telescope fork so as not to produce differential flexure between internal opto-mechanical components during or between observations. An appropriate mechanical design allows the instrument alignment to be highly repeatable and stable for both short and long observation timescales, within a wide-range of temperature variation. NESSI is optically composed of a field lens, a field de-rotator, re-imaging optics, an auto-guider and a Dewar spectrograph that operates at LN2 temperature. In this paper we report on NESSI's detailed mechanical and opto-mechanical design, and the planning for mechanical construction, assembly, integration and verification.

exoplanet↗
NASA NTRS2004

Evolutionary computing for spacecraft power subsystem design search and optimization

Multi-objective optimization involves finding one or more optimal solutions when there is more than one conflicting objective. This means that a solution that is better in one objective compromises or trades-off, other objectives. Trade Studies are conducted by flight projects to create mission concepts with different trade-off solutions for mass, cost, performance and risk.

Hua, Hook↗
DOE OSTI2025

A Joint Search for the Electromagnetic Counterpart to the Gravitational-Wave Binary Black-Hole Merger Candidate S250328ae with the Dark Energy Camera and the Prime Focus Spectrograph

The first detection of an optical counterpart to a gravitational wave signal revealed that collaborative efforts between instruments with different specializations provide a unique opportunity to acquire impactful multi-messenger data. We present results of such a joint search with the Dark Energy Camera (DECam) and Prime Focus Spectrograph (PFS) for the optical counterpart of the LIGO-Virgo-KAGRA event S250328ae, a binary black hole merger candidate of high significance detected at a distance of 511$\pm$82 Mpc and localized within an area of 3 (15) square degrees at 50% (90%) confidence. We observed the 90% confidence area with DECam and identified 36 high-confidence transient candidates after image processing, candidate selection, and candidate vetting. We observed with PFS to obtain optical spectra of DECam candidates, Swift-XRT candidates, and potential host galaxies of S250328ae. In total, 3897 targets were observed by seven pointings covering ~50% of the 90% confidence area. After template fitting and visual inspection, we identified 12 SNe, 159 QSOs, 2975 galaxies, and 131 stars. With the joint observations of DECam and PFS, we found variability in 12 SNe, 139 QSOs, 37 galaxies, and 2 stars. We do not identify any confident optical counterparts, though the association is not ruled out for three variable candidates that are not observed by PFS and 6 QSO candidates without clear variability if the optical counterpart of S250328ae is faint. Despite the lack of confident optical counterparts, this paper serves as a framework for future collaborations between wide-field imagers and multi-object spectrographs to maximize multi-messenger analyses.

Zhang, Haibin [Natl. Astron. Observ. of Japan] (OR↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Implementation of fuel management multi-cycle optimization capabilities in RAVEN optimization framework

Optimization in nuclear fuel-management assists the core reload engineer with finding optimal out-of-core and in-core strategies. RAVEN is INL’s open source software that is equipped with fuel-management optimization capabilities including single-cycle, single- and multi-objective optimization of pressurized water reactors (PWRs) loading patterns (LP) of a fresh core using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II). In practice, however, medium and long term planning of fuel-management needs a multi-cycle approach, where the history and availability of fuel assemblies is considered in the optimization process. In this paper, we present a description of an initial expansion of RAVEN fuel-management optimization capabilities for a multi-cycle optimization framework. N-th cycle optimization capabilities that account for the unique history of recycled fuel assembly in the core were added. The multi-cycle optimization approach taken is formulated as a cycle-wise optimization problem where out-of-core decisions are used to onset each cycle optimization. Out-of-core decisions are managed externally to the in-core optimization by a fuel inventory management module. A proof-of-concept optimization problem is also presented.

42 - ENGINEERING↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗
DOE OSTI2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

Weitz, Jason [UC, San Diego]↗
DOE OSTI2026

Sparse Phase Ansatzes for Resource-Efficient Qudit State Preparation via the SNAP-Displacement Protocol

Efficient preparation of nonclassical bosonic states is a central requirement for quantum computing, simulation, and precision metrology. We study resource-efficient quantum state preparation in bosonic qudit systems using the SNAP-displacement (SD) protocol. Existing SD-based approaches typically require a large number of gates and SNAP phases, resulting in complex control pulses with longer ansatz durations and amplified impact of photon-loss and control errors. In this work, we focus on the near- to medium-term regime, in which noisy quantum devices impose trade-offs on the fidelity that can be achieved, which must be taken into account. Specifically, we propose to optimize only a subset of the SNAP phases and introduce three progressively more general sparse ansatzes. To provide fine-grained control and identify the most suitable ansatz for a given target fidelity, we further employ a scalarized multi-objective optimization that trades off fidelity against either the number of phases or the duration of the ansatz. Numerical results for several target states and qudit dimensions up to $d=64$, evaluated through the hypervolume of the Pareto frontiers, show that these sparse ansatzes achieve favorable trade-offs over the fully parameterized SD protocol in both ideal and noisy settings. The advantage is strongest and most consistent when minimizing the number of phases, while improvements in ansatz duration are smaller and more dependent on the target-state family and noise level, suggesting a practical route to more efficient near- and medium-term bosonic state preparation.

Ferrari Dacrema, Maurizio [Milan Polytechnic] (ORC↗
DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗